{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三维重建之神经辐射场 (NeRF)\n",
    "\n",
    "在这个例子中，我们展示了研究论文 [NeRF](https://arxiv.org/abs/2003.08934) 的最小实现：将场景表示为 Ben Mildenhall 等人的视图合成的神经辐射场。作者提出了一种巧妙的方法，通过神经网络对体积场景函数进行建模，从而合成场景的新视图。\n",
    "\n",
    "为了帮助您直观地理解这一点，让我们从以下问题开始：是否可以将图像中像素的位置提供给神经网络，并要求网络预测该位置的颜色？\n",
    "\n",
    "<figure>\n",
    "<img src=\"images/nerf_pipeline_1.png\" width=\"500\" alt=\"nerf_pipeline_1\">\n",
    "<figcaption>图 1：给定图像坐标的神经网络作为输入并要求预测坐标处的颜色。</figcaption>\n",
    "</figure>\n",
    "\n",
    "神经网络会假设记忆（过拟合）图像。这意味着我们的神经网络会将整个图像编码为其权重。我们可以用每个位置查询神经网络，它最终会重建整个图像。\n",
    "\n",
    "<figure>\n",
    "<img src=\"images/nerf_pipeline_2.png\" width=\"500\" alt=\"nerf_pipeline_1\">\n",
    "<figcaption>图 2：经过训练的神经网络从头开始重新创建图像。</figcaption>\n",
    "</figure>\n",
    "\n",
    "现在出现了一个问题，我们如何扩展这个想法来学习 3D 体积场景？实现与上述类似的过程需要了解每个体素（体积像素）。事实证明，这是一项非常具有挑战性的任务。\n",
    "\n",
    "该论文的作者提出了一种使用场景的一些图像来学习 3D 场景的最小而优雅的方法。他们放弃使用体素进行训练。网络学习对体积场景进行建模，从而生成模型在训练时未显示的 3D 场景的新视图（图像）。\n",
    "\n",
    "需要了解一些先决条件才能充分理解这一过程。我们以这样一种方式构建示例，以便您在开始实施之前拥有所有必需的知识。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"GLOG_v\"] = \"3\"\n",
    "\n",
    "import cv2\n",
    "import json\n",
    "import time\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import mindspore\n",
    "import mindspore as md\n",
    "import mindspore.ops.operations as P\n",
    "from mindspore import nn, ops, Tensor\n",
    "import mindspore as md\n",
    "\n",
    "\n",
    "# environment config\n",
    "mode = \"GRAPH_MODE\"\n",
    "seed = 1\n",
    "device_id = 0\n",
    "device = \"GPU\"\n",
    "init = False\n",
    "\n",
    "# data config\n",
    "half_res = True\n",
    "testskip = 8\n",
    "white_bkgd = True\n",
    "render_test = True\n",
    "\n",
    "# network config\n",
    "chunk = 32768\n",
    "cap_n_samples = 64\n",
    "cap_n_importance = 0\n",
    "netchunk = 65536\n",
    "lrate = 0.0005\n",
    "\n",
    "# runner config\n",
    "cap_n_iters = 3000\n",
    "cap_n_rand = 1024\n",
    "i_testset = 100\n",
    "lrate_decay = 250\n",
    "\n",
    "\n",
    "def context_setup(idx, device='GPU', mode=md.context.GRAPH_MODE):\n",
    "    if init:\n",
    "        return\n",
    "    if device == \"CPU\":\n",
    "        raise NotImplementedError(\"`cumprod` ops does not support CPU\")\n",
    "    md.context.set_context(mode=mode, device_target=device, device_id=idx)\n",
    "\n",
    "md.set_seed(seed)\n",
    "context_setup(device_id, device, getattr(md.context, mode))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 下载并加载数据\n",
    "\n",
    "### 预备知识\n",
    "\n",
    "数据文件包含图像、相机姿势和焦距。这些图像是从多个摄像机角度拍摄的，如图 3 所示。\n",
    "\n",
    "<figure>\n",
    "<img src=\"images/nerf_data_1.png\" width=\"500\" alt=\"nerf_pipeline_1\">\n",
    "<figcaption>图 3：多个摄像机角度。</figcaption>\n",
    "</figure>\n",
    "\n",
    "要在这种情况下理解相机姿势，我们必须首先让自己认为相机是现实世界和二维图像之间的映射。\n",
    "\n",
    "<figure>\n",
    "<img src=\"images/nerf_data_2.png\" width=\"500\" alt=\"nerf_pipeline_1\">\n",
    "<figcaption>图 4：通过相机将 3-D 世界映射到 2-D 图像。</figcaption>\n",
    "</figure>\n",
    "\n",
    "考虑以下等式：\n",
    "\n",
    "$$\n",
    "x = PX\n",
    "$$\n",
    "\n",
    "其中 x 是 2-D 图像点，X 是 3-D 世界点，P 是相机矩阵。P是 一个 3 x 4 矩阵，在将现实世界对象映射到图像平面上起着至关重要的作用。\n",
    "\n",
    "相机矩阵是一个仿射变换矩阵，它与一个 3 x 1 列连接 `[image height, image width, focal length]`以产生姿势矩阵。该矩阵的尺寸为 3 x 5，其中第一个 3 x 3 块位于相机的视点中。轴是 `[down, right, backwards]` 或 `[-y, x, z]` 相机面向前方的位置-z。\n",
    "\n",
    "<figure>\n",
    "<img src=\"images/nerf_data_3.png\" width=\"500\" alt=\"nerf_pipeline_1\">\n",
    "<figcaption>图 5：仿射变换。</figcaption>\n",
    "</figure>\n",
    "\n",
    "COLMAP 帧是 `[right, down, forwards]` 或 `[x, -y, -z]`。[在此处](https://colmap.github.io/)阅读有关 COLMAP 的更多信息。\n",
    "\n",
    "### 下载数据\n",
    "\n",
    "首先自行下载数据: [NeRF Synthetics](https://drive.google.com/file/d/18JxhpWD-4ZmuFKLzKlAw-w5PpzZxXOcG/view?usp=sharing).\n",
    "\n",
    "下载数据后, 按照以下结构安排数据:\n",
    "\n",
    "```text\n",
    ".datasets/\n",
    "└── nerf_synthetics\n",
    "    └── lego\n",
    "        ├── test [600 entries exceeds filelimit, not opening dir]\n",
    "        ├── train [100 entries exceeds filelimit, not opening dir]\n",
    "        ├── transforms_test.json\n",
    "        ├── transforms_train.json\n",
    "        ├── transforms_val.json\n",
    "        └── val [100 entries exceeds filelimit, not opening dir]\n",
    "```\n",
    "\n",
    "### 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datadir = Path(\"data/nerf_synthetic/lego\")\n",
    "train_imgs = list((datadir / \"train\").glob(\"*.png\"))\n",
    "num_images = len(train_imgs)\n",
    "print(num_images)\n",
    "temp_fname = str(train_imgs[np.random.randint(low=0, high=num_images)])\n",
    "tmp_img = cv2.imread(temp_fname, cv2.IMREAD_UNCHANGED)\n",
    "tmp_img = cv2.cvtColor(tmp_img, cv2.COLOR_BGRA2RGBA)\n",
    "\n",
    "plt.imshow(tmp_img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据管线\n",
    "\n",
    "现在您已经了解了相机矩阵的概念以及从 3D 场景到 2D 图像的映射，让我们来谈谈逆映射，即从 2D 图像到 3D 场景。\n",
    "\n",
    "我们需要讨论使用光线投射和追踪的体积渲染，这是常见的计算机图形技术。本节将帮助您快速掌握这些技术。\n",
    "\n",
    "考虑一个带有 `N` 像素的图像。我们通过每个像素射出一条射线，并在射线上采样一些点。射线通常由方程参数化，`r(t) = o + td` 其中 `t` 是参数，`o` 是原点并且 `d` 是单位方向矢量。\n",
    "\n",
    "我们考虑一条射线，并在射线上采样一些随机点。这些采样点每个都有一个独特的位置`(x, y, z)`，并且光​​线有一个视角`(theta, phi)`。视角特别有趣，因为我们可以通过许多不同的方式通过单个像素拍摄光线，每种方式都有独特的视角。这里要注意的另一件有趣的事情是添加到采样过程中的噪声。我们为每个样本添加均匀的噪声，使样本对应于连续分布。这些采样点作为 NeRF 模型的输入。然后要求模型预测该点的 RGB 颜色和体积密度。\n",
    "\n",
    "<figure>\n",
    "<img src=\"images/nerf_data_pipeline.png\" width=\"500\" alt=\"nerf_pipeline_1\">\n",
    "<figcaption>图 6：数据管线</figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded blender (138, 100, 100, 4) (40, 4, 4) [100, 100, 138.88887889922103] data/nerf_synthetic/lego\n"
     ]
    }
   ],
   "source": [
    "def trans_t(t):\n",
    "    return np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, t], [0, 0, 0, 1]],\n",
    "                    dtype=np.float32)\n",
    "\n",
    "\n",
    "def rot_phi(phi):\n",
    "    return np.array([[1, 0, 0, 0], [0, np.cos(phi), -np.sin(phi), 0],\n",
    "                     [0, np.sin(phi), np.cos(phi), 0], [0, 0, 0, 1]],\n",
    "                    dtype=np.float32)\n",
    "\n",
    "\n",
    "def rot_theta(th):\n",
    "    return np.array([[np.cos(th), 0, -np.sin(th), 0], [0, 1, 0, 0],\n",
    "                     [np.sin(th), 0, np.cos(th), 0], [0, 0, 0, 1]],\n",
    "                    dtype=np.float32)\n",
    "\n",
    "\n",
    "def pose_spherical(theta, phi, radius):\n",
    "    \"\"\"pose_spherical\"\"\"\n",
    "    c2w = trans_t(radius)\n",
    "    c2w = np.matmul(rot_phi(phi / 180. * np.pi), c2w)\n",
    "    c2w = np.matmul(rot_theta(theta / 180. * np.pi), c2w)\n",
    "    c2w = np.matmul(\n",
    "        np.array([[-1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]],\n",
    "                 dtype=np.float32), c2w)\n",
    "    return c2w\n",
    "\n",
    "\n",
    "def load_blender_data(basedir, half_res=False, testskip=1):\n",
    "    \"\"\"load_blender_data\"\"\"\n",
    "    splits = ['train', 'val', 'test']\n",
    "    metas = {}\n",
    "    for s in splits:\n",
    "        with open(os.path.join(basedir, f'transforms_{s}.json'),\n",
    "                  'r',\n",
    "                  encoding='utf-8') as fp:\n",
    "            metas[s] = json.load(fp)\n",
    "\n",
    "    all_imgs = []\n",
    "    all_poses = []\n",
    "    counts = [0]\n",
    "    for s in splits:\n",
    "        meta = metas[s]\n",
    "        imgs = []\n",
    "        poses = []\n",
    "        if s == 'train' or testskip == 0:\n",
    "            skip = 1\n",
    "        else:\n",
    "            skip = testskip\n",
    "\n",
    "        for frame in meta['frames'][::skip]:\n",
    "            fname = os.path.join(basedir, frame['file_path'] + '.png')\n",
    "            tmp_img = cv2.imread(fname, cv2.IMREAD_UNCHANGED)\n",
    "            tmp_img = cv2.cvtColor(tmp_img, cv2.COLOR_BGRA2RGBA)\n",
    "            imgs.append(tmp_img)\n",
    "            poses.append(np.array(frame['transform_matrix']))\n",
    "        imgs = (np.array(imgs) / 255.).astype(\n",
    "            np.float32)  # keep all 4 channels (RGBA)\n",
    "        poses = np.array(poses).astype(np.float32)\n",
    "        counts.append(counts[-1] + imgs.shape[0])\n",
    "        all_imgs.append(imgs)\n",
    "        all_poses.append(poses)\n",
    "\n",
    "    i_split = [np.arange(counts[i], counts[i + 1]) for i in range(3)]\n",
    "\n",
    "    imgs = np.concatenate(all_imgs, 0)\n",
    "    poses = np.concatenate(all_poses, 0)\n",
    "\n",
    "    cap_h, cap_w = imgs[0].shape[:2]\n",
    "    camera_angle_x = float(meta['camera_angle_x'])\n",
    "    focal = .5 * cap_w / np.tan(.5 * camera_angle_x)\n",
    "\n",
    "    render_poses = np.stack([\n",
    "        pose_spherical(angle, -30.0, 4.0)\n",
    "        for angle in np.linspace(-180, 180, 40 + 1)[:-1]\n",
    "    ], axis=0)\n",
    "\n",
    "    if half_res:\n",
    "        cap_h = cap_h // 8\n",
    "        cap_w = cap_w // 8\n",
    "        focal = focal / 8.\n",
    "\n",
    "        imgs_half_res = np.zeros((imgs.shape[0], cap_h, cap_w, 4))\n",
    "        for i, img in enumerate(imgs):\n",
    "            imgs_half_res[i] = cv2.resize(img, (cap_h, cap_w),\n",
    "                                          interpolation=cv2.INTER_AREA)\n",
    "        imgs = imgs_half_res\n",
    "\n",
    "    return md.Tensor(imgs).astype(\"float32\"), md.Tensor(poses).astype(\n",
    "        \"float32\"), md.Tensor(render_poses).astype(\"float32\"), [\n",
    "            cap_h, cap_w, focal\n",
    "        ], i_split\n",
    "\n",
    "\n",
    "images, poses, render_poses, hwf, i_split = load_blender_data(datadir, half_res, testskip)\n",
    "print('Loaded blender', images.shape, render_poses.shape, hwf, datadir)\n",
    "i_train, i_val, i_test = i_split\n",
    "\n",
    "near = 2.\n",
    "far = 6.\n",
    "\n",
    "if white_bkgd:\n",
    "    images = images[..., :3] * images[..., -1:] + (1. - images[..., -1:])\n",
    "else:\n",
    "    images = images[..., :3]\n",
    "\n",
    "if render_test:\n",
    "    render_poses = poses[i_test.tolist()]\n",
    "\n",
    "# Cast intrinsics to right types\n",
    "cap_h, cap_w, focal = hwf\n",
    "cap_h, cap_w = int(cap_h), int(cap_w)\n",
    "\n",
    "hwf = [cap_h, cap_w, focal]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NeRF 模型\n",
    "\n",
    "该模型是一个多层感知器 (MLP)，以 ReLU 作为其非线性。\n",
    "\n",
    "论文摘录：\n",
    "\n",
    "“我们通过限制网络将体积密度 sigma 预测为仅位置的函数来鼓励表示是多视图一致的 x，同时允许将 RGB 颜色c预测为位置和查看方向的函数。为此， MLP 首先使用 8 个全连接层（使用 ReLU 激活和每层 256 个通道）处理输入 3D 坐标，并输出 sigma 和 256 维特征向量。然后将该特征向量与相机光线的观察方向连接并传递到一个额外的全连接层（使用 ReLU 激活和 128 个通道），输出与视图相关的 RGB 颜色。”\n",
    "\n",
    "<figure>\n",
    "<img src=\"images/nerf_model.png\" width=\"500\" alt=\"nerf_pipeline_1\">\n",
    "<figcaption>图 7：NeRF 模型</figcaption>\n",
    "</figure>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeRFMLP(nn.Cell):\n",
    "    \"\"\"\n",
    "    NeRF MLP architecture.\n",
    "\n",
    "    Args:\n",
    "        cap_d (int, optional): Model depth. Default: 8.\n",
    "        cap_w (int, optional): Model width. Default: 256.\n",
    "        input_ch (int, optional): Input channel. Default: 3.\n",
    "        input_ch_views (int, optional): Input view channel. Default: 3.\n",
    "        output_ch (int, optional): Output channel. Default: 4.\n",
    "        skips (tuple, optional): Skip connection layer index. Default: (4).\n",
    "        use_view_dirs (bool, optional): Use view directions or not. Default: False.\n",
    "\n",
    "    Inputs:\n",
    "        - **x** (Tensor) - Query tensors. points and view directions (..., 6).\n",
    "\n",
    "    Outputs:\n",
    "        Tensor, query features (..., feature_dims).\n",
    "\n",
    "    Supported Platforms:\n",
    "        ``Ascend`` ``GPU`` ``CPU``\n",
    "\n",
    "    Examples:\n",
    "        >>> model = NeRFMLP()\n",
    "        >>> inputs = ms.numpy.randn(1, 3)\n",
    "        >>> outputs = model(inputs)\n",
    "        [[0.2384 0.8456 0.6845 0.1584]]\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "            self,\n",
    "            cap_d=8,\n",
    "            cap_w=256,\n",
    "            input_ch=3,\n",
    "            input_ch_views=3,\n",
    "            output_ch=4,\n",
    "            skips=(4),\n",
    "            use_view_dirs=False,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.cap_d = cap_d\n",
    "        self.cap_w = cap_w\n",
    "        self.input_ch = input_ch\n",
    "        self.input_ch_views = input_ch_views\n",
    "        self.skips = skips\n",
    "        self.use_view_dirs = use_view_dirs\n",
    "\n",
    "        self.pts_linears = nn.CellList([nn.Dense(in_channels=input_ch, out_channels=cap_w)] + [\n",
    "            nn.Dense(in_channels=cap_w, out_channels=cap_w) if i not in\n",
    "            self.skips else nn.Dense(in_channels=cap_w + input_ch, out_channels=cap_w) for i in range(cap_d - 1)\n",
    "        ])\n",
    "\n",
    "        self.views_linears = nn.CellList([nn.Dense(in_channels=input_ch_views + cap_w, out_channels=cap_w // 2)])\n",
    "\n",
    "        if use_view_dirs:\n",
    "            self.feature_linear = nn.Dense(in_channels=cap_w, out_channels=cap_w)\n",
    "            self.alpha_linear = nn.Dense(in_channels=cap_w, out_channels=1)\n",
    "            self.rgb_linear = nn.Dense(in_channels=cap_w // 2, out_channels=3)\n",
    "        else:\n",
    "            self.output_linear = nn.Dense(in_channels=cap_w, out_channels=output_ch)\n",
    "\n",
    "    def construct(self, x):\n",
    "        \"\"\"NeRF MLP construct\"\"\"\n",
    "        input_pts, input_views = x[..., :self.input_ch], x[..., self.input_ch:]\n",
    "        h = input_pts\n",
    "        for i, _ in enumerate(self.pts_linears):\n",
    "            h = self.pts_linears[i](h)\n",
    "            h = P.ReLU()(h)\n",
    "            if i in self.skips:\n",
    "                h = P.Concat(-1)([input_pts, h])\n",
    "\n",
    "        if self.use_view_dirs:\n",
    "            alpha = self.alpha_linear(h)\n",
    "            feature = self.feature_linear(h)\n",
    "            h = P.Concat(-1)([feature, input_views])\n",
    "\n",
    "            for i, _ in enumerate(self.views_linears):\n",
    "                h = self.views_linears[i](h)\n",
    "                h = P.ReLU()(h)\n",
    "\n",
    "            rgb = self.rgb_linear(h)\n",
    "            outputs = P.Concat(-1)([rgb, alpha])\n",
    "        else:\n",
    "            outputs = self.output_linear(h)\n",
    "\n",
    "        return outputs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可微分渲染器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VolumeRenderer(nn.Cell):\n",
    "    \"\"\"\n",
    "    Volume Renderer architecture.\n",
    "\n",
    "    Args:\n",
    "        chunk (int): Number of rays processed in parallel, decrease if running out of memory.\n",
    "        cap_n_samples (int): Number of coarse samples per ray for coarse net.\n",
    "        cap_n_importance (int): Number of additional fine samples per ray for fine net.\n",
    "        net_chunk (int): Number of pts sent through network in parallel, decrease if running out of memory.\n",
    "        white_bkgd (bool): Set to render synthetic data on a white background (always use for DeepVoxels).\n",
    "        model_coarse (nn.Cell): Coarse net.\n",
    "        model_fine (nn.Cell, optional): Fine net, or None.\n",
    "        embedder_p (Dict): Config for positional encoding for point.\n",
    "        embedder_d (Dict): Config for positional encoding for view direction.\n",
    "        near (float, optional): The near plane. Default: 0.0.\n",
    "        far (float, optional): The far plane. Default: 1e6.\n",
    "\n",
    "    Inputs:\n",
    "        - **rays** (Tensor) - The ray tensor. (..., num_pts_per_ray, ray_batch_dims).\n",
    "\n",
    "    Outputs:\n",
    "        Tuple of 2 Tensor, the output tensors.\n",
    "\n",
    "        - **fine_net_output** (Tensor, optional) - The fine net output features.\n",
    "        - **coarse_net_output** (Tensor) - The coarse net output features.\n",
    "\n",
    "    Supported Platforms:\n",
    "        ``Ascend`` ``GPU`` ``CPU``\n",
    "\n",
    "    Examples:\n",
    "        >>> model = VolumeRenderer(1000, 6, 12, 1000, False, P.Identity(), P.Identity(), P.Identity(), P.Identity())\n",
    "        >>> inputs = ms.numpy.randn(1, 1, 3)\n",
    "        >>> outputs = model(inputs)\n",
    "        ([[0.2384 0.8456 0.1273]], [[0.8653 0.1866 0.6382]])\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self,\n",
    "                 chunk,\n",
    "                 cap_n_samples,\n",
    "                 cap_n_importance,\n",
    "                 net_chunk,\n",
    "                 white_bkgd,\n",
    "                 model_coarse,\n",
    "                 model_fine,\n",
    "                 embedder_p,\n",
    "                 embedder_d,\n",
    "                 near=0.0,\n",
    "                 far=1e6):\n",
    "        super().__init__()\n",
    "\n",
    "        self.chunk = chunk\n",
    "        self.cap_n_samples = cap_n_samples\n",
    "        self.cap_n_importance = cap_n_importance\n",
    "        self.net_chunk = net_chunk\n",
    "        self.white_bkgd = white_bkgd\n",
    "\n",
    "        self.model_coarse = model_coarse\n",
    "        self.model_fine = model_fine\n",
    "        # embedder for positions\n",
    "        self.embedder_p = Embedder(**embedder_p)\n",
    "        # embedder for view-in directions\n",
    "        self.embedder_d = Embedder(**embedder_d)\n",
    "\n",
    "        self.near = near\n",
    "        self.far = far\n",
    "\n",
    "    def construct(self, rays):\n",
    "        \"\"\"Volume renderer construct.\"\"\"\n",
    "        return self.inference(rays)\n",
    "\n",
    "    def inference(self, rays):\n",
    "        \"\"\"Volume renderer inference.\"\"\"\n",
    "        # make the number of rays be multiple of the chunk size\n",
    "        cap_n_rays = (rays.shape[1] // self.chunk + 1) * self.chunk\n",
    "        cap_n = self.cap_n_samples\n",
    "\n",
    "        res_ls = {\"rgb_map_coarse\": [], \"rgb_map_fine\": []}\n",
    "\n",
    "        for i in range(0, cap_n_rays, self.chunk):\n",
    "            ray_origins, ray_dirs = rays[:, i:i + self.chunk, :]\n",
    "            reshape_op = mindspore.ops.Reshape()\n",
    "            view_dirs = reshape_op(\n",
    "                ray_dirs / mindspore.numpy.norm(ray_dirs, axis=-1, keepdims=True),\n",
    "                (-1, 3),\n",
    "            )\n",
    "\n",
    "            near, far = self.near * mindspore.numpy.ones_like(ray_dirs[..., :1]), self.far * mindspore.numpy.ones_like(\n",
    "                ray_dirs[..., :1])\n",
    "            cap_m = ray_origins.shape[0]\n",
    "            if cap_m == 0:\n",
    "                continue\n",
    "\n",
    "            # stratified sampling along rays\n",
    "            s_samples = sample_along_rays(near, far, cap_n)\n",
    "\n",
    "            # position samples along rays\n",
    "            unsqueeze_op = P.ExpandDims()\n",
    "            pos_samples = unsqueeze_op(ray_origins,\n",
    "                                       1) + unsqueeze_op(ray_dirs, 1) * unsqueeze_op(s_samples, 2)\n",
    "            # expand ray directions to the same shape of samples\n",
    "            expand_op = P.BroadcastTo(pos_samples.shape)\n",
    "            dir_samples = expand_op(unsqueeze_op(view_dirs, 1))\n",
    "\n",
    "            reshape_op = P.Reshape()\n",
    "            pos_samples = reshape_op(pos_samples, (-1, 3))\n",
    "            dir_samples = reshape_op(dir_samples, (-1, 3))\n",
    "\n",
    "            # retrieve optic data from the network\n",
    "            optic_d = self._run_network_model_coarse(pos_samples, dir_samples)\n",
    "            optic_d = mindspore.numpy.reshape(optic_d, [cap_m, cap_n, 4])\n",
    "\n",
    "            # composite optic data to generate a RGB image\n",
    "            rgb_map_coarse, weights_coarse = self._composite(optic_d, s_samples, ray_dirs)\n",
    "\n",
    "            if self.cap_n_importance > 0:\n",
    "                z_vals_mid = 0.5 * (s_samples[..., 1:] + s_samples[..., :-1])\n",
    "                z_samples = sample_pdf(z_vals_mid, weights_coarse[..., 1:-1], self.cap_n_importance)\n",
    "                z_samples = mindspore.ops.stop_gradient(z_samples)\n",
    "\n",
    "                sort_op = P.Sort(axis=-1)\n",
    "                z_vals, _ = sort_op(P.Concat(-1)([s_samples, z_samples]))\n",
    "                pts = (ray_origins[..., None, :] + ray_dirs[..., None, :] * z_vals[..., :, None]\n",
    "                      )\n",
    "\n",
    "                expand_op_2 = P.BroadcastTo(pts.shape)\n",
    "                dir_samples = expand_op_2(unsqueeze_op(view_dirs, 1))\n",
    "\n",
    "                pts = reshape_op(pts, (-1, 3))\n",
    "                dir_samples = reshape_op(dir_samples, (-1, 3))\n",
    "\n",
    "                optic_d = self._run_network_model_fine(pts, dir_samples)\n",
    "                optic_d = reshape_op(optic_d, (cap_m, cap_n + self.cap_n_importance, 4))\n",
    "\n",
    "                rgb_map_fine, _ = self._composite(optic_d, z_vals, ray_dirs)\n",
    "            else:\n",
    "                rgb_map_fine = rgb_map_coarse\n",
    "\n",
    "            res_ls[\"rgb_map_coarse\"].append(rgb_map_coarse)\n",
    "            res_ls[\"rgb_map_fine\"].append(rgb_map_fine)\n",
    "\n",
    "        res = {}\n",
    "        for k, v in res_ls.items():\n",
    "            res[k] = P.Concat(0)(v)\n",
    "\n",
    "        return res[\"rgb_map_fine\"], res[\"rgb_map_coarse\"]\n",
    "\n",
    "    def _run_network_model_fine(self, pts, view_dirs):\n",
    "        \"\"\"Run fine model.\"\"\"\n",
    "        inputs_flat = pts\n",
    "        embedded = self.embedder_p(inputs_flat)\n",
    "\n",
    "        if view_dirs is not None:\n",
    "            input_dirs_flat = view_dirs\n",
    "            embedded_dirs = self.embedder_d(input_dirs_flat)\n",
    "            embedded = P.Concat(-1)([embedded, embedded_dirs])\n",
    "\n",
    "        chunk = self.net_chunk\n",
    "        outputs_flat_ls = []\n",
    "        for i in range(0, embedded.shape[0], chunk):\n",
    "            outputs_flat_ls.append(self.model_fine(embedded[i:i + chunk]))\n",
    "        outputs_flat = P.Concat(0)(outputs_flat_ls)\n",
    "        return outputs_flat\n",
    "\n",
    "    def _run_network_model_coarse(self, pts, view_dirs):\n",
    "        \"\"\"Run coarse model.\"\"\"\n",
    "        inputs_flat = pts\n",
    "        embedded = self.embedder_p(inputs_flat)\n",
    "\n",
    "        if view_dirs is not None:\n",
    "            input_dirs_flat = view_dirs\n",
    "            embedded_dirs = self.embedder_d(input_dirs_flat)\n",
    "            embedded = P.Concat(-1)([embedded, embedded_dirs])\n",
    "\n",
    "        chunk = self.net_chunk\n",
    "        outputs_flat_ls = []\n",
    "        for i in range(0, embedded.shape[0], chunk):\n",
    "            outputs_flat_ls.append(self.model_coarse(embedded[i:i + chunk]))\n",
    "        outputs_flat = P.Concat(0)(outputs_flat_ls)\n",
    "        return outputs_flat\n",
    "\n",
    "    def _transfer(self, optic_d, dists):\n",
    "        \"\"\"Transfer occupancy to alpha values.\"\"\"\n",
    "        sigmoid = P.Sigmoid()\n",
    "        rgbs = sigmoid(optic_d[..., :3])\n",
    "        alphas = 1.0 - P.Exp()(-1.0 * (P.ReLU()(optic_d[(..., 3)])) * dists)\n",
    "\n",
    "        return rgbs, alphas\n",
    "\n",
    "    def _composite(self, optic_d, s_samples, rays_d):\n",
    "        \"\"\"Composite the colors and densities.\"\"\"\n",
    "        # distances between each samples\n",
    "        dists = s_samples[..., 1:] - s_samples[..., :-1]\n",
    "        dists_list = (\n",
    "            dists,\n",
    "            (mindspore.numpy.ones([]) * 1e10).expand_as(dists[..., :1]),\n",
    "        )\n",
    "        dists = P.Concat(-1)(dists_list)\n",
    "\n",
    "        dists = dists * mindspore.numpy.norm(rays_d[..., None, :], axis=-1)\n",
    "\n",
    "        # retrieve display colors and alphas for each samples by a transfer function\n",
    "        rgbs, alphas = self._transfer(optic_d, dists)\n",
    "\n",
    "        weights = alphas * mindspore.numpy.cumprod(\n",
    "            P.Concat(-1)([mindspore.numpy.ones((alphas.shape[0], 1)), 1.0 - alphas + 1e-10])[:, :-1],\n",
    "            axis=-1,\n",
    "        )\n",
    "        sum_op = mindspore.ops.ReduceSum()\n",
    "        rgb_map = sum_op(weights[..., None] * rgbs, -2)\n",
    "        acc_map = sum_op(weights, -1)\n",
    "\n",
    "        if self.white_bkgd:\n",
    "            rgb_map = rgb_map + (1.0 - acc_map[..., None])\n",
    "\n",
    "        return rgb_map, weights\n",
    "\n",
    "class Embedder(nn.Cell):\n",
    "    \"\"\"\n",
    "    Embedder for positional embedding.\n",
    "\n",
    "    Args:\n",
    "        input_dims (int): Input dimensions.\n",
    "        max_freq_pow (float): Maximum frequency pow.\n",
    "        num_freqs (int): Number of frequencies.\n",
    "        periodic_fns (list, optional): Periodic fns. Default: [mindspore.ops.Sin(), mindspore.ops.Cos()].\n",
    "        log_sampling (bool, optional): Log sampling. Default: True.\n",
    "        include_input (bool, optional): Include input or not. Default: True.\n",
    "\n",
    "    Inputs:\n",
    "        inputs (Tensor) - Input tensor.\n",
    "\n",
    "    Outputs:\n",
    "        Tensor, input concatenated with positional embeddings.\n",
    "\n",
    "    Supported Platforms:\n",
    "        ``Ascend`` ``GPU`` ``CPU``\n",
    "\n",
    "    Examples:\n",
    "        >>> model = Embedder(1, 1)\n",
    "        >>> inputs = ms.numpy.randn(1)\n",
    "        >>> outputs = model(inputs)\n",
    "        [0.1384 0.4426]\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "            self,\n",
    "            input_dims,\n",
    "            max_freq_pow,\n",
    "            num_freqs,\n",
    "            periodic_fns=(mindspore.ops.Sin(), mindspore.ops.Cos()),\n",
    "            log_sampling=True,\n",
    "            include_input=True,\n",
    "    ):\n",
    "        super().__init__()\n",
    "\n",
    "        embed_fns = []\n",
    "        out_dims = 0\n",
    "        if include_input:\n",
    "            embed_fns.append(mindspore.ops.Identity())\n",
    "            out_dims += input_dims\n",
    "\n",
    "        if log_sampling:\n",
    "            freq_bands = mindspore.Tensor(2.0)**mindspore.numpy.linspace(0.0, max_freq_pow, num=num_freqs)\n",
    "        else:\n",
    "            freq_bands = mindspore.numpy.linspace(2.0**0.0, 2.0**max_freq_pow, num=num_freqs)\n",
    "\n",
    "        for _ in freq_bands:\n",
    "            for p_fn in periodic_fns:\n",
    "                embed_fns.append(p_fn)\n",
    "                out_dims += input_dims\n",
    "\n",
    "        self.embed_fns = embed_fns\n",
    "        self.out_dims = out_dims\n",
    "\n",
    "        self.freq_bands = freq_bands\n",
    "\n",
    "    def construct(self, inputs):\n",
    "        \"\"\"Embedder construct.\"\"\"\n",
    "        out = []\n",
    "        for i, fn in enumerate(self.embed_fns):\n",
    "            if i == 0:\n",
    "                out.append(fn(inputs))\n",
    "            else:\n",
    "                out.append(fn(inputs * self.freq_bands[(i - 1) // 2]))\n",
    "        return P.Concat(-1)(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No ckpt reloaded\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CustomTrainOneStepCell<\n",
       "  (network): RendererWithCriterion<\n",
       "    (renderer): VolumeRenderer<\n",
       "      (model_coarse): NeRFMLP<\n",
       "        (pts_linears): CellList<\n",
       "          (0): Dense<input_channels=63, output_channels=256, has_bias=True>\n",
       "          (1): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (2): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (3): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (4): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (5): Dense<input_channels=319, output_channels=256, has_bias=True>\n",
       "          (6): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (7): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          >\n",
       "        (views_linears): CellList<\n",
       "          (0): Dense<input_channels=283, output_channels=128, has_bias=True>\n",
       "          >\n",
       "        (feature_linear): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "        (alpha_linear): Dense<input_channels=256, output_channels=1, has_bias=True>\n",
       "        (rgb_linear): Dense<input_channels=128, output_channels=3, has_bias=True>\n",
       "        >\n",
       "      (embedder_p): Embedder<>\n",
       "      (embedder_d): Embedder<>\n",
       "      >\n",
       "    (loss_fn): MSELoss<>\n",
       "    >\n",
       "  (optimizer): Adam<>\n",
       "  >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_embedder(multi_res, i=0):\n",
    "    \"\"\"\n",
    "    Get embedder function.\n",
    "\n",
    "    Args:\n",
    "        multi_res (int): Log2 of max freq for positional encoding.\n",
    "        i (int, optional): Set 0 for default positional encoding, -1 for none. Default: 0.\n",
    "\n",
    "    Returns:\n",
    "        Tuple of nn.Cell and int, embedder and the output dimensions.\n",
    "\n",
    "        - **embedder** (nn.Cell) - The embedder.\n",
    "        - **out_dims** (int) - The output dimensions.\n",
    "    \"\"\"\n",
    "    if i == -1:\n",
    "        return md.ops.Identity(), 3\n",
    "\n",
    "    embed_kwargs = {\n",
    "        \"include_input\": True,\n",
    "        \"input_dims\": 3,\n",
    "        \"max_freq_pow\": multi_res - 1,\n",
    "        \"num_freqs\": multi_res,\n",
    "        \"log_sampling\": True,\n",
    "        \"periodic_fns\": [md.ops.Sin(), md.ops.Cos()],\n",
    "    }\n",
    "\n",
    "    embedder_obj = Embedder(**embed_kwargs)\n",
    "    embed = embed_kwargs\n",
    "    return embed, embedder_obj.out_dims\n",
    "\n",
    "\n",
    "def create_nerf(multires=10,\n",
    "                i_embed=0,\n",
    "                multires_views=4,\n",
    "                netdepth=8,\n",
    "                netwidth=256,\n",
    "                netdepth_fine=8,\n",
    "                netwidth_fine=256,\n",
    "                use_view_dirs=True,\n",
    "                cap_n_importance=0,\n",
    "                ckpt_path=None):\n",
    "    \"\"\"create nerf model and load weights\"\"\"\n",
    "    embed_fn, input_ch = get_embedder(multires, i_embed)\n",
    "\n",
    "    input_ch_views = 0\n",
    "    embeddirs_fn = None\n",
    "    if use_view_dirs:\n",
    "        embeddirs_fn, input_ch_views = get_embedder(multires_views,\n",
    "                                                    i_embed)\n",
    "    # Create networks\n",
    "    output_ch = 4\n",
    "    skips = [4]\n",
    "    model_coarse = NeRFMLP(cap_d=netdepth,\n",
    "                           cap_w=netwidth,\n",
    "                           input_ch=input_ch,\n",
    "                           output_ch=output_ch,\n",
    "                           skips=skips,\n",
    "                           input_ch_views=input_ch_views,\n",
    "                           use_view_dirs=use_view_dirs)\n",
    "    grad_vars = [{\"params\": model_coarse.trainable_params()}]\n",
    "\n",
    "    model_fine = None\n",
    "    if cap_n_importance > 0:\n",
    "        model_fine = NeRFMLP(cap_d=netdepth_fine,\n",
    "                             cap_w=netwidth_fine,\n",
    "                             input_ch=input_ch,\n",
    "                             output_ch=output_ch,\n",
    "                             skips=skips,\n",
    "                             input_ch_views=input_ch_views,\n",
    "                             use_view_dirs=use_view_dirs)\n",
    "        grad_vars += [{\"params\": model_fine.trainable_params()}]\n",
    "\n",
    "    optimizer = None\n",
    "    start_iter = 0\n",
    "\n",
    "    # Load checkpoints\n",
    "    if ckpt_path is not None:\n",
    "        print(\"Reloading from\", ckpt_path)\n",
    "        ckpt = md.load_checkpoint(ckpt_path)\n",
    "\n",
    "        # Load training steps\n",
    "        start_iter = int(ckpt[\"global_steps\"]) + 1\n",
    "\n",
    "        # Load network weights\n",
    "        md.load_param_into_net(\n",
    "            model_coarse,\n",
    "            {key: value for key, value in ckpt.items() if \".model_coarse.\" in key},\n",
    "        )\n",
    "        if model_fine is not None:\n",
    "            md.load_param_into_net(\n",
    "                model_fine,\n",
    "                {key: value for key, value in ckpt.items() if \".model_fine.\" in key},\n",
    "            )\n",
    "    else:\n",
    "        print(\"No ckpt reloaded\")\n",
    "\n",
    "    return start_iter, optimizer, model_coarse, model_fine, embed_fn, embeddirs_fn\n",
    "\n",
    "class RendererWithCriterion(nn.Cell):\n",
    "    \"\"\"\n",
    "    Renderer with criterion.\n",
    "\n",
    "    Args:\n",
    "        renderer (nn.Cell): Renderer.\n",
    "        loss_fn (nn.Cell, optional): Loss function. Default: nn.MSELoss().\n",
    "\n",
    "    Inputs:\n",
    "        - **rays** (Tensor) - Rays tensor.\n",
    "        - **gt** (Tensor) - Ground truth tensor.\n",
    "\n",
    "    Outputs:\n",
    "        Tensor, loss for one forward pass.\n",
    "    \"\"\"\n",
    "    def __init__(self, renderer, loss_fn=nn.MSELoss()):\n",
    "        \"\"\"Renderer with criterion.\"\"\"\n",
    "        super().__init__()\n",
    "        self.renderer = renderer\n",
    "        self.loss_fn = loss_fn\n",
    "\n",
    "    def construct(self, rays, gt):\n",
    "        \"\"\"Renderer Trainer construct.\"\"\"\n",
    "        rgb_map_fine, rgb_map_coarse = self.renderer(rays)\n",
    "        return self.loss_fn(rgb_map_fine, gt) + self.loss_fn(rgb_map_coarse, gt)\n",
    "\n",
    "\n",
    "# Create nerf model\n",
    "start_iter, optimizer, model_coarse, model_fine, embed_fn, embeddirs_fn = create_nerf()\n",
    "# Training steps\n",
    "global_steps = start_iter\n",
    "# Create volume renderer\n",
    "renderer = VolumeRenderer(chunk, cap_n_samples,\n",
    "                          cap_n_importance, netchunk,\n",
    "                          white_bkgd, model_coarse, model_fine,\n",
    "                          embed_fn, embeddirs_fn, near, far)\n",
    "\n",
    "renderer.model_coarse.to_float(md.dtype.float16)\n",
    "if renderer.model_fine is not None:\n",
    "    renderer.model_fine.to_float(md.dtype.float16)\n",
    "\n",
    "renderer_with_criteron = RendererWithCriterion(renderer)\n",
    "optimizer = nn.Adam(params=renderer.trainable_params(),\n",
    "                    learning_rate=lrate,\n",
    "                    beta1=0.9,\n",
    "                    beta2=0.999)\n",
    "\n",
    "grad_scale = ops.MultitypeFuncGraph(\"grad_scale\")\n",
    "@grad_scale.register(\"Tensor\", \"Tensor\")\n",
    "def gradient_scale(scale, grad):\n",
    "    return grad * ops.cast(scale, ops.dtype(grad))\n",
    "\n",
    "class CustomTrainOneStepCell(nn.TrainOneStepCell):\n",
    "    def __init__(self, network, optimizer, sens=1.0):\n",
    "        super(CustomTrainOneStepCell, self).__init__(network, optimizer, sens)\n",
    "        self.hyper_map = ops.HyperMap()\n",
    "        self.reciprocal_sense = Tensor(1 / sens, mindspore.float32)\n",
    "\n",
    "    def scale_grad(self, gradients):\n",
    "        gradients = self.hyper_map(ops.partial(grad_scale, self.reciprocal_sense), gradients)\n",
    "        return gradients\n",
    "\n",
    "    def construct(self, *inputs):\n",
    "        loss = self.network(*inputs)\n",
    "        sens = ops.fill(loss.dtype, loss.shape, self.sens)\n",
    "        # calculate gradients, the sens will equal to the loss_scale\n",
    "        grads = self.grad(self.network, self.weights)(*inputs, sens)\n",
    "        # gradients / loss_scale\n",
    "        grads = self.scale_grad(grads)\n",
    "        # reduce gradients in distributed scenarios\n",
    "        grads = self.grad_reducer(grads)\n",
    "        loss = ops.depend(loss, self.optimizer(grads))\n",
    "        return loss\n",
    "\n",
    "loss_scale = 1024.0\n",
    "train_renderer = CustomTrainOneStepCell(renderer_with_criteron, optimizer, loss_scale)\n",
    "train_renderer.set_train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iter 1:   0%|          | 0/3000 [00:00<?, ?it/s][ERROR] CORE(185816,7fcd3b55d4c0,python):2023-04-15-15:18:01.097.783 [mindspore/core/utils/file_utils.cc:254] GetRealPath] Get realpath failed, path[/tmp/ipykernel_185816/2949924921.py]\n",
      "[ERROR] CORE(185816,7fcd3b55d4c0,python):2023-04-15-15:18:01.097.941 [mindspore/core/utils/file_utils.cc:254] GetRealPath] Get realpath failed, path[/tmp/ipykernel_185816/2949924921.py]\n",
      "Iter 3000: 100%|██████████| 3000/3000 [06:28<00:00,  7.72it/s, loss=0.0133, psnr=18.7, time=0.111] \n"
     ]
    }
   ],
   "source": [
    "def generate_rays(h, w, f, pose):\n",
    "    '''\n",
    "    Given an image plane, generate rays from the camera origin to each pixel on the image plane.\n",
    "\n",
    "    Arguments:\n",
    "        h: height of the image plane.\n",
    "        w: width of the image plane.\n",
    "        f: focal length of the image plane.\n",
    "        pose: the extrinsic parameters of the camera. (3, 4) or (4, 4)\n",
    "\n",
    "    Returns:\n",
    "        A tuple: origins of rays, directions of rays\n",
    "    '''\n",
    "\n",
    "    # Coordinates of the 2D grid\n",
    "    cols = md.ops.ExpandDims()(\n",
    "        md.numpy.linspace(-1.0 * w / 2, w - 1 - w / 2, w) / f,\n",
    "        0).repeat(h, axis=0)  # (h, w)\n",
    "    rows = md.ops.ExpandDims()(\n",
    "        -1.0 * md.numpy.linspace(-1.0 * h / 2, h - 1 - h / 2, h) / f,\n",
    "        1).repeat(w, axis=1)  # (h, w)\n",
    "\n",
    "    # Ray directions for all pixels\n",
    "    ray_dirs = md.numpy.stack([cols, rows, -1.0 * md.numpy.ones_like(cols)],\n",
    "                              axis=-1)  # (h, w, 3)\n",
    "    # Apply rotation transformation to make each ray orient according to the camera\n",
    "    unsqueeze_op = md.ops.ExpandDims()\n",
    "    ray_dirs = md.numpy.sum(unsqueeze_op(ray_dirs, 2) * pose[:3, :3], axis=-1)\n",
    "    # Origin position\n",
    "    rays_oris = pose[:3, -1].expand_as(ray_dirs)  # (h, w, 3)\n",
    "\n",
    "    return rays_oris, ray_dirs.astype(pose.dtype)  # (h, w, 3), (h, w, 3)\n",
    "\n",
    "\n",
    "def train_net(iter_, train_renderer, optimizer, rays, gt):\n",
    "    '''\n",
    "    Train a network.\n",
    "\n",
    "    Arguments:\n",
    "        config: configuration.\n",
    "        iter_: current iterations.\n",
    "        renderer: a volume renderer.\n",
    "        optimizer: a network optimizer.\n",
    "        rays: a batch of rays for training. (#rays * #samples, 6)\n",
    "        gt: the groundtruth.\n",
    "\n",
    "    Returns:\n",
    "        A tuple: (MSE loss, PSNR).\n",
    "    '''\n",
    "    loss = train_renderer(rays, gt)\n",
    "\n",
    "    # Update learning rate\n",
    "    decay_rate = 0.1\n",
    "    decay_steps = lrate_decay * 1000\n",
    "    new_lrate = lrate * (decay_rate**(iter_ / decay_steps))\n",
    "    optimizer.learning_rate = md.Parameter(new_lrate)\n",
    "\n",
    "    return float(loss), float(psnr_from_mse(loss))\n",
    "\n",
    "\n",
    "def test_net(img_h,\n",
    "             img_w,\n",
    "             focal,\n",
    "             renderer,\n",
    "             test_poses,\n",
    "             gt=None,\n",
    "             on_progress=None,\n",
    "             on_complete=None):\n",
    "    '''\n",
    "    Test the network and generate results.\n",
    "\n",
    "    Arguments:\n",
    "        img_h: height of image plane.\n",
    "        img_w: width of image plane.\n",
    "        focal: focal length.\n",
    "        renderer: the volume renderer.\n",
    "        test_poses: poses used to test the network. (#poses, 4, 4)\n",
    "        on_progress: a callback function invoked per generation of a result.\n",
    "        on_complete: a callback function invoked after generating all results.\n",
    "\n",
    "    Returns:\n",
    "        A tuple: (Mean test time, MSE loss, PSNR).\n",
    "    '''\n",
    "\n",
    "    rgb_maps = []\n",
    "    loss_ls = []\n",
    "    psnr_ls = []\n",
    "    time_ls = []\n",
    "\n",
    "    reshape_op = md.ops.Reshape()\n",
    "    stack_op = md.ops.Stack(axis=0)\n",
    "\n",
    "    image_list = []\n",
    "\n",
    "    for j, test_pose in enumerate(test_poses):\n",
    "        t0 = time.time()\n",
    "\n",
    "        # Generate rays for all pixels\n",
    "        ray_oris, ray_dirs = generate_rays(img_h, img_w, focal, test_pose)\n",
    "        ray_oris = reshape_op(ray_oris, (-1, 3))\n",
    "        ray_dirs = reshape_op(ray_dirs, (-1, 3))\n",
    "        test_batch_rays = stack_op([ray_oris, ray_dirs])\n",
    "\n",
    "        # Retrieve testing results\n",
    "        rgb_map, _ = renderer.inference(test_batch_rays)\n",
    "        rgb_map = reshape_op(rgb_map, (img_h, img_w, 3))\n",
    "        rgb_maps.append(rgb_map.asnumpy())\n",
    "\n",
    "        # If given groundtruth, compute MSE and PSNR\n",
    "        if gt is not None:\n",
    "            loss = mse(rgb_map, gt[j])\n",
    "            psnr = psnr_from_mse(loss)\n",
    "            loss_ls.append(float(loss))\n",
    "            psnr_ls.append(float(psnr))\n",
    "\n",
    "        time_ls.append(time.time() - t0)\n",
    "\n",
    "        # Handle each testing result\n",
    "        if on_progress:\n",
    "            if isinstance(on_progress, list):\n",
    "                on_progress[0](j, rgb_maps[-1])\n",
    "                if gt is not None:\n",
    "                    on_progress[1](j, gt[j].asnumpy())\n",
    "            else:\n",
    "                on_progress(j, rgb_maps[-1])\n",
    "\n",
    "        image_list.append(to8b(rgb_maps[-1]))\n",
    "\n",
    "    # Handle all testing results\n",
    "    if on_complete:\n",
    "        on_complete(np.stack(rgb_maps, 0))\n",
    "\n",
    "    if not loss_ls:\n",
    "        loss_ls = [0.0]\n",
    "    if not psnr_ls:\n",
    "        psnr_ls = [0.0]\n",
    "    if not time_ls:\n",
    "        time_ls = [0.0]\n",
    "\n",
    "    return np.mean(time_ls), np.mean(loss_ls), np.mean(psnr_ls), image_list\n",
    "\n",
    "\n",
    "def to8b(x):\n",
    "    \"\"\"Convert normalized color to 8-bit color\"\"\"\n",
    "    return (255 * np.clip(x, 0.0, 1.0)).astype(np.uint8)\n",
    "\n",
    "\n",
    "def mse(im1, im2):\n",
    "    '''\n",
    "    MSE between two images.\n",
    "    '''\n",
    "\n",
    "    return md.numpy.mean((im1 - im2)**2)\n",
    "\n",
    "\n",
    "psnr_from_mse_base = md.Tensor([10.0])\n",
    "\n",
    "\n",
    "def psnr_from_mse(v):\n",
    "    '''\n",
    "    Convert MSE to PSNR.\n",
    "    '''\n",
    "    return -10.0 * (md.numpy.log(v) / md.numpy.log(psnr_from_mse_base))\n",
    "\n",
    "\n",
    "def sample_grid_2d(cap_h, cap_w, cap_n):\n",
    "    \"\"\"\n",
    "    Sample cells in an cap_h x cap_w mesh grid.\n",
    "\n",
    "    Args:\n",
    "        cap_h (int): Height of the mesh grid.\n",
    "        cap_w (int): Width of the mesh grid.\n",
    "        cap_n (int): The number of samples.\n",
    "\n",
    "    Returns:\n",
    "        Tuple of 2 Tensor, sampled rows and sampled columns.\n",
    "\n",
    "        - **select_coords_x** (Tensor) - Sampled rows.\n",
    "        - **select_coords_y** (Tensor) - Sampled columns.\n",
    "    \"\"\"\n",
    "    if cap_n > cap_w * cap_h:\n",
    "        cap_n = cap_w * cap_h\n",
    "\n",
    "    # Create a 2D mesh grid where each element is the coordinate of the cell\n",
    "    stack_op = md.ops.Stack(-1)\n",
    "    coords = stack_op(\n",
    "        md.numpy.meshgrid(\n",
    "            md.numpy.linspace(0, cap_h - 1, cap_h),\n",
    "            md.numpy.linspace(0, cap_w - 1, cap_w),\n",
    "            indexing=\"ij\",\n",
    "        ))\n",
    "    # Flat the mesh grid\n",
    "    coords = md.ops.Reshape()(coords, (-1, 2))\n",
    "    # Sample N cells in the mesh grid\n",
    "    select_indexes = np.random.choice(coords.shape[0],\n",
    "                                      size=[cap_n],\n",
    "                                      replace=False)\n",
    "    # Sample N cells among the mesh grid\n",
    "    select_coords = coords[select_indexes.tolist()].astype(\"int32\")\n",
    "\n",
    "    return select_coords[:, 0], select_coords[:, 1]\n",
    "\n",
    "\n",
    "def sample_along_rays(near,\n",
    "                      far,\n",
    "                      cap_cap_n_samples,\n",
    "                      lin_disp=False,\n",
    "                      perturb=True):\n",
    "    \"\"\"\n",
    "    Sample points along rays.\n",
    "\n",
    "    Args:\n",
    "        near (Tensor): A vector containing nearest point for each ray. (cap_n_rays).\n",
    "        far (Tensor): A vector containing furthest point for each ray. (cap_n_rays).\n",
    "        cap_n_samples (int): The number of sampled points for each ray.\n",
    "        lin_disp (bool): True for sample linearly in inverse depth rather than in depth (used for some datasets).\n",
    "        perturb (bool): True for stratified sampling. False for uniform sampling.\n",
    "\n",
    "    Returns:\n",
    "        Tensor, samples where j-th component of the i-th row is the j-th sampled position along the i-th ray.\n",
    "    \"\"\"\n",
    "    # The number of rays\n",
    "    cap_n_rays = near.shape[0]\n",
    "\n",
    "    # Uniform samples along rays\n",
    "    t_vals = md.numpy.linspace(0.0, 1.0, num=cap_cap_n_samples)\n",
    "    if not lin_disp:\n",
    "        z_vals = near * (1.0 - t_vals) + far * t_vals\n",
    "    else:\n",
    "        z_vals = 1.0 / (1.0 / near * (1.0 - t_vals) + 1.0 / far * t_vals)\n",
    "\n",
    "    expand_op = md.ops.BroadcastTo((cap_n_rays, cap_cap_n_samples))\n",
    "    z_vals = expand_op(z_vals)\n",
    "\n",
    "    if perturb:\n",
    "        # Get intervals between samples\n",
    "        mids = 0.5 * (z_vals[..., 1:] + z_vals[..., :-1])\n",
    "        cat_op = md.ops.Concat(-1)\n",
    "        upper = cat_op([mids, z_vals[..., -1:]])\n",
    "        lower = cat_op([z_vals[..., :1], mids])\n",
    "        # Stratified samples in those intervals\n",
    "        t_rand = md.numpy.rand(z_vals.shape)\n",
    "        z_vals = lower + (upper - lower) * t_rand\n",
    "\n",
    "    return z_vals\n",
    "\n",
    "\n",
    "def sample_pdf(bins, weights, cap_cap_n_samples, det=False):\n",
    "    \"\"\"\n",
    "    Sample pdf function.\n",
    "\n",
    "    Args:\n",
    "        bins (int): The number of bins for pdf.\n",
    "        weights (Tensor): The estimated weights.\n",
    "        cap_cap_n_samples (int): The number of points to be sampled.\n",
    "        det (bool, optional): Deterministic run or not. Default: False.\n",
    "\n",
    "    Returns:\n",
    "        Tensor, sampled pdf tensor.\n",
    "    \"\"\"\n",
    "    weights = weights + 1e-5\n",
    "    pdf = weights / md.numpy.sum(weights, -1, keepdims=True)\n",
    "    cdf = md.numpy.cumsum(pdf, -1)\n",
    "    cdf = md.ops.Concat(-1)([md.numpy.zeros_like(cdf[..., :1]), cdf])\n",
    "\n",
    "    # Take uniform samples\n",
    "    temp_shape = cdf.shape[:-1]\n",
    "    cap_cap_n_samples_new = cap_cap_n_samples\n",
    "    temp_shape_new = list(temp_shape) + [cap_cap_n_samples_new]\n",
    "    if det:\n",
    "        u = md.numpy.linspace(0.0, 1.0, num=cap_cap_n_samples)\n",
    "        expand_op = md.ops.BroadcastTo(temp_shape_new)\n",
    "        u = expand_op(u)\n",
    "    else:\n",
    "        u = md.numpy.rand(temp_shape_new)\n",
    "\n",
    "    # Invert CDF\n",
    "    indexes = nd_searchsorted(cdf, u)\n",
    "\n",
    "    below = md.numpy.maximum(md.numpy.zeros_like(indexes - 1), indexes - 1)\n",
    "    above = md.numpy.minimum((cdf.shape[-1] - 1) * md.numpy.ones_like(indexes),\n",
    "                             indexes)\n",
    "    indexes_g = md.ops.Stack(axis=-1)([below, above])\n",
    "\n",
    "    matched_shape = (indexes_g.shape[0], indexes_g.shape[1], cdf.shape[-1])\n",
    "    gather_op = md.ops.GatherD()\n",
    "    unsqueeze_op = md.ops.ExpandDims()\n",
    "    expand_op = md.ops.BroadcastTo(matched_shape)\n",
    "    cdf_g = gather_op(expand_op(unsqueeze_op(cdf, 1)), 2, indexes_g)\n",
    "    bins_g = gather_op(expand_op(unsqueeze_op(bins, 1)), 2, indexes_g)\n",
    "\n",
    "    denom = cdf_g[..., 1] - cdf_g[..., 0]\n",
    "    denom = md.numpy.where(denom < 1e-5, md.numpy.ones_like(denom), denom)\n",
    "    t = (u - cdf_g[..., 0]) / denom\n",
    "    samples = bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0])\n",
    "\n",
    "    return samples\n",
    "\n",
    "\n",
    "def nd_searchsorted(cdf, u):\n",
    "    \"\"\"N-dim searchsorted.\n",
    "\n",
    "    Args:\n",
    "        cdf (Tensor): The cdf sampling weights.\n",
    "        u (Tensor): The interval tensors.\n",
    "\n",
    "    Returns:\n",
    "        Tensor, index after searchsorted ops.\n",
    "    \"\"\"\n",
    "    spatial_shape = cdf.shape[:-1]\n",
    "    last_dim_cdf, last_dim_u = cdf.shape[-1], u.shape[-1]\n",
    "    cdf_, u_ = cdf.view(-1, last_dim_cdf), u.view(-1, last_dim_u)\n",
    "    indexes_ls = []\n",
    "\n",
    "    for i in range(cdf_.shape[0]):\n",
    "        indexes_ls.append(cdf_[i].searchsorted(u_[i], side=\"right\"))\n",
    "    indexes = md.ops.Stack(axis=0)(indexes_ls)\n",
    "    indexes = indexes.view(*spatial_shape, last_dim_u)\n",
    "    return indexes\n",
    "\n",
    "train_image_list = []\n",
    "with tqdm(range(1, cap_n_iters + 1)) as pbar:\n",
    "    pbar.n = start_iter\n",
    "\n",
    "    for i in pbar:\n",
    "        # Show progress\n",
    "        pbar.set_description(f'Iter {global_steps + 1:d}')\n",
    "        pbar.update()\n",
    "\n",
    "        # Start time of the current iteration\n",
    "        time0 = time.time()\n",
    "\n",
    "        img_i = int(np.random.choice(i_train))\n",
    "\n",
    "        target = images[img_i]\n",
    "        pose = poses[img_i, :3, :4]\n",
    "\n",
    "        if cap_n_rand is not None:\n",
    "            rays_o, rays_d = generate_rays(\n",
    "                cap_h, cap_w, focal,\n",
    "                pose)  # (cap_h, cap_w, 3), (cap_h, cap_w, 3)\n",
    "            sampled_rows, sampled_cols = sample_grid_2d(\n",
    "                cap_h, cap_w, cap_n_rand)\n",
    "            rays_o = rays_o[sampled_rows, sampled_cols]  # (cap_n_rand, 3)\n",
    "            rays_d = rays_d[sampled_rows, sampled_cols]  # (cap_n_rand, 3)\n",
    "\n",
    "            batch_rays = md.ops.Stack(axis=0)([rays_o, rays_d])\n",
    "            target_s = target[sampled_rows, sampled_cols]  # (cap_n_rand, 3)\n",
    "\n",
    "        loss, psnr = train_net(global_steps, train_renderer, optimizer,\n",
    "                               batch_rays, target_s)\n",
    "\n",
    "        pbar.set_postfix(time=time.time() - time0, loss=loss, psnr=psnr)\n",
    "\n",
    "        # Save testing results\n",
    "        if (global_steps + 1) % i_testset == 0:\n",
    "            test_idx = np.random.randint(low=0, high=len(i_test))\n",
    "            test_time, test_loss, test_psnr, sub_train_image_list = test_net(\n",
    "                cap_h, cap_w, focal, renderer,\n",
    "                poses[i_test[test_idx:test_idx + 1].tolist()],\n",
    "                images[i_test[test_idx:test_idx + 1].tolist()])\n",
    "            train_image_list.extend(sub_train_image_list)\n",
    "        global_steps += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "%matplotlib inline\n",
    "\n",
    "def showGif(image_list, name):\n",
    "    show_list = []\n",
    "    fig = plt.figure(figsize=(4, 4), dpi=120)\n",
    "    for epoch in range(len(image_list)):\n",
    "        plt.axis(\"off\")\n",
    "        show_list.append([plt.imshow(image_list[epoch])])\n",
    "\n",
    "    ani = animation.ArtistAnimation(fig, show_list, interval=1000, repeat_delay=1000, blit=True)\n",
    "    ani.save(f'images/{name}.gif', writer='pillow', fps=1)\n",
    "\n",
    "showGif(train_image_list, \"nerf.train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![train.git](images/nerf.train.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 渲染"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_time, test_loss, test_psnr, test_image_list = test_net(\n",
    "    cap_h,\n",
    "    cap_w,\n",
    "    focal,\n",
    "    renderer,\n",
    "    md.Tensor(poses[i_test.tolist()]),\n",
    "    images[i_test.tolist()])\n",
    "print(\n",
    "    f\"Testing results: [ Mean Time: {test_time:.4f}s, Loss: {test_loss:.4f}, PSNR: {test_psnr:.4f} ]\"\n",
    ")\n",
    "showGif(test_image_list, \"nerf.test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![train.git](images/nerf.test.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 推理\n",
    "\n",
    "使用预训练权重推理最佳结果。建议重启 jupyter kernel 后运行以下代码块，避免内存不足问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/yangchen/projects/mindspore/nerf_mindspore/src/nerf_utils/engine_utils.py:164: UserWarning: Not support N-D searchsorted, set `max_call_depth=20000` to prevent `for loop` collapse.\n",
      "  warnings.warn(\"Not support N-D searchsorted, set `max_call_depth=20000` to prevent `for loop` collapse.\")\n",
      ">>> Loading dataset\n",
      "Loaded blender (138, 400, 400, 4) (40, 3, 4) [400, 400, 555.5555155968841] /home/yangchen/projects/mindspore/nerf_mindspore/data/nerf_synthetic/lego\n",
      "TRAIN views: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
      " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
      " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95\n",
      " 96 97 98 99]\n",
      "TEST views: [113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130\n",
      " 131 132 133 134 135 136 137]\n",
      "VAL views: [100 101 102 103 104 105 106 107 108 109 110 111 112]\n",
      ">>> Saving checkpoints and results in ./results/blender_lego_coarse_0\n",
      ">>> Creating models\n",
      "Found ckpts ['../ckpts/200000.blender_lego_coarse_0.ckpt']\n",
      "Reloading from ../ckpts/200000.blender_lego_coarse_0.ckpt\n",
      "use fp16.\n",
      ">>> Render only\n",
      "Rendering (iter=200000):\n",
      "100%|███████████████████████████████████████████| 25/25 [28:13<00:00, 67.73s/it]\n",
      "Testing results: [ Mean Time: 67.6248s, Loss: 0.0010, PSNR: 30.0631 ]\n"
     ]
    }
   ],
   "source": [
    "device = \"GPU\"\n",
    "device_id = 0\n",
    "mode = \"GRAPH_MODE\"\n",
    "\n",
    "# 下载权重\n",
    "!if [[ ! -f ckpts/200000.blender_lego_coarse_0.ckpt ]]; then mkdir -p ckpts/ && wget -P ckpts/ https://download.mindspore.cn/vision/nerf/lego/200000.blender_lego_coarse_0.ckpt; fi\n",
    "# 运行推理脚本\n",
    "!cd src && GLOG_v=3 python eval.py --name lego_coarse --data_dir /home/yangchen/projects/mindspore/nerf_mindspore/data/nerf_synthetic/lego --dataset_type blender --half_res --cap_n_rand 4096 --cap_n_samples 192 --cap_n_importance 0 --use_view_dirs --raw_noise_std 1e0 --white_bkgd --device {device} --device_id {device_id} --precision fp16 --mode {mode} --render_test --ckpt ../ckpts/200000.blender_lego_coarse_0.ckpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_185816/3136349309.py:17: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
      "  inference_image_list = [imageio.imread(image_path) for image_path in sorted(glob.glob(\"src/results/blender_lego_coarse_0/renderonly_200000/*.png\"))]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "import imageio\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "%matplotlib inline\n",
    "\n",
    "def showGif(image_list, name):\n",
    "    show_list = []\n",
    "    fig = plt.figure(figsize=(4, 4), dpi=120)\n",
    "    for epoch in range(len(image_list)):\n",
    "        plt.axis(\"off\")\n",
    "        show_list.append([plt.imshow(image_list[epoch])])\n",
    "\n",
    "    ani = animation.ArtistAnimation(fig, show_list, interval=1000, repeat_delay=1000, blit=True)\n",
    "    ani.save(f'images/{name}.gif', writer='pillow', fps=1)\n",
    "\n",
    "inference_image_list = [imageio.imread(image_path) for image_path in sorted(glob.glob(\"src/results/blender_lego_coarse_0/renderonly_200000/*.png\"))]\n",
    "showGif(inference_image_list, \"nerf.inference\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![inference.gif](images/nerf.inference.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "本案例对 NeRF 的论文中提出的模型进行了详细的解释，向读者完整地展现了该算法的流程。如需查看详细代码，可参考 `src/`。"
   ]
  },
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   "metadata": {},
   "source": [
    "## 引用\n",
    "\n",
    "[1] Mildenhall, Ben et al. “NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis.” ECCV (2020).\n",
    "\n",
    "### 参考资料\n",
    "\n",
    "- [NeRF 仓库](https://github.com/bmild/nerf)：官方 NeRF 的存储库。\n",
    "\n",
    "- [NeRF 视频](https://www.youtube.com/watch?v=dPWLybp4LL0)：关于 NeRF 的讲解视频。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'skimage'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m----> 4\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mskimage\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmeasure\u001b[39;00m \u001b[39mimport\u001b[39;00m compare_ssim\n\u001b[1;32m      5\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mskimage\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmeasure\u001b[39;00m \u001b[39mimport\u001b[39;00m compare_psnr\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'skimage'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mskimage\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmeasure\u001b[39;00m \u001b[39mimport\u001b[39;00m compare_psnr\n\u001b[1;32m      6\u001b[0m \u001b[39mexcept\u001b[39;00m:\n\u001b[0;32m----> 7\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mskimage\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmetrics\u001b[39;00m \u001b[39mimport\u001b[39;00m structural_similarity\n\u001b[1;32m      8\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mskimage\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmetrics\u001b[39;00m \u001b[39mimport\u001b[39;00m peak_signal_noise_ratio \u001b[39mas\u001b[39;00m compare_psnr\n\u001b[1;32m     10\u001b[0m     \u001b[39mdef\u001b[39;00m \u001b[39mcompare_ssim\u001b[39m(gt, img, win_size, multichannel\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'skimage'"
     ]
    }
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   "source": []
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